146 research outputs found
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques
(AI) from the research community and industry to tackle "real world" problems,
requires High Performance Computing (HPC) resources to efficiently compute and
scale complex algorithms across thousands of nodes. Unfortunately, typical data
scientists are not familiar with the unique requirements and characteristics of
HPC environments. They usually develop their applications with high-level
scripting languages or frameworks such as TensorFlow and the installation
process often requires connection to external systems to download open source
software during the build. HPC environments, on the other hand, are often based
on closed source applications that incorporate parallel and distributed
computing API's such as MPI and OpenMP, while users have restricted
administrator privileges, and face security restrictions such as not allowing
access to external systems. In this paper we discuss the issues associated with
the deployment of AI frameworks in a secure HPC environment and how we
successfully deploy AI frameworks on SuperMUC-NG with Charliecloud.Comment: 6 pages, 2 figures, 2019 IEEE High Performance Extreme Computing
Conferenc
Conditional Progressive Generative Adversarial Network for satellite image generation
Image generation and image completion are rapidly evolving fields, thanks to
machine learning algorithms that are able to realistically replace missing
pixels. However, generating large high resolution images, with a large level of
details, presents important computational challenges. In this work, we
formulate the image generation task as completion of an image where one out of
three corners is missing. We then extend this approach to iteratively build
larger images with the same level of detail. Our goal is to obtain a scalable
methodology to generate high resolution samples typically found in satellite
imagery data sets. We introduce a conditional progressive Generative
Adversarial Networks (GAN), that generates the missing tile in an image, using
as input three initial adjacent tiles encoded in a latent vector by a
Wasserstein auto-encoder. We focus on a set of images used by the United
Nations Satellite Centre (UNOSAT) to train flood detection tools, and validate
the quality of synthetic images in a realistic setup.Comment: Published at the SyntheticData4ML Neurips worksho
Validation of Deep Convolutional Generative Adversarial Networks for High Energy Physics Calorimeter Simulations
In particle physics the simulation of particle transport through detectors
requires an enormous amount of computational resources, utilizing more than 50%
of the resources of the CERN Worldwide Large Hadron Collider Grid. This
challenge has motivated the investigation of different, faster approaches for
replacing the standard Monte Carlo simulations. Deep Learning Generative
Adversarial Networks are among the most promising alternatives. Previous
studies showed that they achieve the necessary level of accuracy while
decreasing the simulation time by orders of magnitudes. In this paper we
present a newly developed neural network architecture which reproduces a
three-dimensional problem employing 2D convolutional layers and we compare its
performance with an earlier architecture consisting of 3D convolutional layers.
The performance evaluation relies on direct comparison to Monte Carlo
simulations, in terms of different physics quantities usually employed to
quantify the detector response. We prove that our new neural network
architecture reaches a higher level of accuracy with respect to the 3D
convolutional GAN while reducing the necessary computational resources.
Calorimeters are among the most expensive detectors in terms of simulation
time. Therefore we focus our study on an electromagnetic calorimeter prototype
with a regular highly granular geometry, as an example of future calorimeters.Comment: AAAI-MLPS 2021 Spring Symposium at Stanford Universit
Convolutional LSTM models to estimate network traffic
Network utilisation efficiency can, at least in principle, often be improved
by dynamically re-configuring routing policies to better distribute on-going
large data transfers. Unfortunately, the information necessary to decide on an
appropriate reconfiguration - details of on-going and upcoming data transfers
such as their source and destination and, most importantly, their volume and
duration - is usually lacking. Fortunately, the increased use of scheduled
transfer services, such as FTS, makes it possible to collect the necessary
information. However, the mere detection and characterisation of larger
transfers is not sufficient to predict with confidence the likelihood a network
link will become overloaded. In this paper we present the use of LSTM-based
models (CNN-LSTM and Conv-LSTM) to effectively estimate future network traffic
and so provide a solid basis for formulating a sensible network configuration
plan.Comment: vCHEP2021 conference proceeding
Precise Image Generation on Current Noisy Quantum Computing Devices
The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning
model designed to generate accurate images on current Noise Intermediate Scale
(NISQ) Quantum devices. Variational quantum circuits form the core of the QAG
model, and various circuit architectures are evaluated. In combination with the
so-called MERA-upsampling architecture, the QAG model achieves excellent
results, which are analyzed and evaluated in detail. To our knowledge, this is
the first time that a quantum model has achieved such accurate results. To
explore the robustness of the model to noise, an extensive quantum noise study
is performed. In this paper, it is demonstrated that the model trained on a
physical quantum device learns the noise characteristics of the hardware and
generates outstanding results. It is verified that even a quantum hardware
machine calibration change during training of up to 8% can be well tolerated.
For demonstration, the model is employed in indispensable simulations in high
energy physics required to measure particle energies and, ultimately, to
discover unknown particles at the Large Hadron Collider at CERN
Resource Saving via Ensemble Techniques for Quantum Neural Networks
Quantum neural networks hold significant promise for numerous applications,
particularly as they can be executed on the current generation of quantum
hardware. However, due to limited qubits or hardware noise, conducting
large-scale experiments often requires significant resources. Moreover, the
output of the model is susceptible to corruption by quantum hardware noise. To
address this issue, we propose the use of ensemble techniques, which involve
constructing a single machine learning model based on multiple instances of
quantum neural networks. In particular, we implement bagging and AdaBoost
techniques, with different data loading configurations, and evaluate their
performance on both synthetic and real-world classification and regression
tasks. To assess the potential performance improvement under different
environments, we conduct experiments on both simulated, noiseless software and
IBM superconducting-based QPUs, suggesting these techniques can mitigate the
quantum hardware noise. Additionally, we quantify the amount of resources saved
using these ensemble techniques. Our findings indicate that these methods
enable the construction of large, powerful models even on relatively small
quantum devices.Comment: Extended paper of the work presented at QTML 2022. Close to published
versio
Quantum Machine Learning in High Energy Physics
Machine learning has been used in high energy physics since a long time,
primarily at the analysis level with supervised classification. Quantum
computing was postulated in the early 1980s as way to perform computations that
would not be tractable with a classical computer. With the advent of noisy
intermediate-scale quantum computing devices, more quantum algorithms are being
developed with the aim at exploiting the capacity of the hardware for machine
learning applications. An interesting question is whether there are ways to
combine quantum machine learning with High Energy Physics. This paper reviews
the first generation of ideas that use quantum machine learning on problems in
high energy physics and provide an outlook on future applications.Comment: 25 pages, 9 figures, submitted to Machine Learning: Science and
Technology, Focus on Machine Learning for Fundamental Physics collectio
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
Deep learning is finding its way into high energy physics by replacing
traditional Monte Carlo simulations. However, deep learning still requires an
excessive amount of computational resources. A promising approach to make deep
learning more efficient is to quantize the parameters of the neural networks to
reduced precision. Reduced precision computing is extensively used in modern
deep learning and results to lower execution inference time, smaller memory
footprint and less memory bandwidth. In this paper we analyse the effects of
low precision inference on a complex deep generative adversarial network model.
The use case which we are addressing is calorimeter detector simulations of
subatomic particle interactions in accelerator based high energy physics. We
employ the novel Intel low precision optimization tool (iLoT) for quantization
and compare the results to the quantized model from TensorFlow Lite. In the
performance benchmark we gain a speed-up of 1.73x on Intel hardware for the
quantized iLoT model compared to the initial, not quantized, model. With
different physics-inspired self-developed metrics, we validate that the
quantized iLoT model shows a lower loss of physical accuracy in comparison to
the TensorFlow Lite model.Comment: Submitted at ICPRAM 2021; from CERN openlab - Intel collaboratio
Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
Free energy-based reinforcement learning (FERL) with clamped quantum
Boltzmann machines (QBM) was shown to significantly improve the learning
efficiency compared to classical Q-learning with the restriction, however, to
discrete state-action space environments. In this paper, the FERL approach is
extended to multi-dimensional continuous state-action space environments to
open the doors for a broader range of real-world applications. First, free
energy-based Q-learning is studied for discrete action spaces, but continuous
state spaces and the impact of experience replay on sample efficiency is
assessed. In a second step, a hybrid actor-critic scheme for continuous
state-action spaces is developed based on the Deep Deterministic Policy
Gradient algorithm combining a classical actor network with a QBM-based critic.
The results obtained with quantum annealing, both simulated and with D-Wave
quantum annealing hardware, are discussed, and the performance is compared to
classical reinforcement learning methods. The environments used throughout
represent existing particle accelerator beam lines at the European Organisation
for Nuclear Research (CERN). Among others, the hybrid actor-critic agent is
evaluated on the actual electron beam line of the Advanced Plasma Wakefield
Experiment (AWAKE).Comment: 17 pages, 15 figures, to be submitted to "Quantum" journa
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